TY  - JOUR
Y1  - 2022///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142689514&doi=10.1007%2f978-981-16-2183-3_100&partnerID=40&md5=02c6bb86b5ca5aa7836c0d0d6095447a
A1  - Alazzawi, A.K.
A1  - Rais, H.M.
A1  - Basri, S.
A1  - Alsariera, Y.A.
A1  - Imam, A.A.
A1  - Abed, S.A.
A1  - Balogun, A.O.
A1  - Kumar, G.
JF  - Lecture Notes in Electrical Engineering
VL  - 758
N2  - In software testing, test case generation is the most challenging activities and expensive tasks. Where has a considerable impact on the ability to produce a desired or intended result (i.e., quality and efficacy) of software testing. As a result, several researchers have developed a number of t-way test case generation strategies (where t points the interaction strength between parameters) due to the market demand to the various types of tests based on different approaches. This paper presents an orchestrated survey of the latest test case generation strategies such as Binary Black Hole (BBH), Sine Cosine Variable Strength (SCAVS), Combinatorial Testing Based Jaya Algorithm (CTJ), deterministic genetic multi-parameter-order (GAMIPOG) and Hybrid Artificial Bee Colony (HABC). This survey illustrates the strengths and weaknesses of each strategy, and indicates potential research studies in the field for future work. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
KW  - Ability testing; Optimization; Surveys
KW  -  Black holes; Combinatorial testing; Interaction strength; Market demand; Metaheuristic; Optimization algorithms; Software testings; T-way testing; Test case generation; Test generations
KW  -  Software testing
ID  - scholars17414
SN  - 18761100
PB  - Springer Science and Business Media Deutschland GmbH
EP  - 1060
AV  - none
N1  - cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
TI  - Recent t-way Test Generation Strategies Based on Optimization Algorithms: An Orchestrated Survey
SP  - 1055
ER  -